Business intelligence (BI) is an umbrella term that includes the applications, infrastructure, tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.

In order to realize a successful BI project of any scope within the context of any organization, this requires close cooperation and mutual understanding between the organizational entities producing the information (IT), analyzing the information (Business), and taking decisions based on the information (Management).

 

 

Business and Management also need to be made clear that information is not readily available as raw material, but rather the result of an often-complex production process tailored to the specific environment of the organization.

BI project members quickly face the complexity of gaining the required insights in, and actively managing the overview of, both the problem space and the solution space related to this core process of turning data into information and knowledge.

The objective of EA4BI is to offer assistance by expanding the discipline of enterprise architecture modeling to BI. As an enterprise architecture framework and toolkit focused on BI, it facilitates and empowers end-to-end BI modeling, but also provides interfaces with other enterprise architecture domains and modeling languages.

 

EA4BI is a four-layered enterprise architecture framework for BI, taking into consideration the three major perspectives enterprise architecture offers and adding a fourth one that allows for realizing the connection with technical design. The four layers are enterprise architecture, solution architecture, technical architecture and technical design.

 

 

 

Enterprise Architecture (EA) takes place at a high level in the organizational structure. Strategy and concepts are strongly focused in this discipline, with very limited attention for concrete technology choices and implementation details. In order to support this conceptual level, EA4BI provides the viewpoints of Ross & Weill Core Diagram and Analytical Master Data Management.

Solution Architecture (SA) is deliberately positioned at the center. This discipline translates the enterprise architecture into a time-based project portfolio and facilitates the selection of appropriate technology, thereby allowing for the implementation of the organizational strategy. In order to support this concrete level, EA4BI provides the viewpoints of Five-Layered BI Architecture, Data Vault 2.0 Architecture, Inmon Corporate Information Factory (CIF), and Kimball Data Warehouse Architecture.

Technical Architecture (TA) takes a deep dive into the technology for a given project scope, as such preparing it for detailed design and implementation. In order to support this practical level, EA4BI provides the viewpoints of Technical Reference Architecture (TRA) and Technical Solution Architecture (TSA).

Technical Design (TD) enables the linkage of detailed technical design with the previous architectural layers. Where the latter offers guidance and black-box views (still hiding the implementation details), TD offers a white-box view (revealing the implementation details) and actually controls the implementation done by the development team. EA4BI offers the viewpoints of Dimensional Modeling and Data Vault 2.0 Modeling, in order to provide support for both mainstream methodologies. The Extract Transform Load (ETL) viewpoint allows to describe the complexities of data retrieval, data processing and data storage.

 

The first version of EA4BI offers 11 viewpoints, covering all four layers of the framework. This paragraph gives an overview and provides the rationale for incorporating the viewpoints.

 

1. Ross & Weill Core Diagram: a simple one-page picture that provides a high-level view of the process, data, and technologies constituting the desired foundation for execution. It helps Management, Business and IT to understand the required enterprise architecture for the organization. The early identification of processes and data provides a good starting point the definition of a BI architecture
   
   
2. Analytical Master Data Management: its primary purpose is to improve analytics, reports, and business intelligence in general. An analytical master data capability, adequately managed by Business and IT, results in the identification and the provisioning of a single point of reference for the critical data, required by an organization in order to support decision making by Management. As such, it elaborates and details the core data present on the Ross & Weill Core Diagram, and represents an important foundational building block of the BI architecture

Solution Architecture

3. Five-Layered BI Architecture: a reference architecture for BI. As it is methodology-neutral, conceptual by nature, and defined at the level of solution architecture, it can serve as a good starting point for the elaboration of the BI solution space and the resulting BI architecture, without having to make an early “dogmatic” choice between the major BI methodologies represented by the other viewpoints in this architectural layer
   
   
4. Data Vault 2.0 Architecture: the solution architecture component of the Data Vault 2.0 methodology. It allows for the creation of a BI architecture based on this methodology
   
   
5. Inmon Corporate Information Factory (CIF): the solution architecture component of the Inmon Data Warehouse 2.0 methodology. It allows for the creation of a BI architecture based on this methodology
   
   
6. Kimball Data Warehouse Architecture: the solution architecture component of the of the Kimball Data Warehouse Toolkit methodology. It allows for the creation of a BI architecture based on this methodology

Technical Architecture

7. Technical Reference Architecture (TRA): allows an organization to define a platform and technology dependent technical BI reference architecture, as such providing an overarching guidance to all BI projects
   
   
8. Technical Solution Architecture (TSA): elaborates the platform and technology dependent technical BI architecture for a given project scope. It can be based on the aforementioned TRA or created ad hoc

Technical Design

9. Dimensional Modeling: a database design technique for modeling data warehouses and data marts, and part of the Kimball Data Warehouse Toolkit. It allows for the creation of technical designs based on this methodology
   
   
10. Data Vault 2.0 Modeling: a database design technique for modeling data vaults, and part of the Data Vault 2.0 methodology. It allows for the creation of technical designs based on this methodology
    
    
11. ETL Modeling: allows depicting the Extract Transform Load (ETL) processes that enable the data flows in the BI ecosystem of an organization. The purpose of this viewpoint is not to describe all ETL flows, but to select those that expose high complexity and/or have a major impact on the BI landscape

The following example overview diagram illustrates the usage of EA4BI across all the layers: the three major perspectives enterprise architecture offers (enterprise, solution, technical) and a fourth one providing linkage to technical design.

EA4BI provides an appropriate set of relationships that offer full traceability between all the elements present on all the viewpoints that are part of the four layers.

Traceability between the three architectural layers is achieved by means of the relationship types composition and realization. Traceability towards the technical design layer is realized by means of a dependency.

Linkage with other domains and languages

EA4BI offers two extensibility features:

• Association: same level of detail
• Decomposition: higher level of detail

The former would manifest itself by creating an EA4BI relationship between an EA4BI element and/or diagram and a non-EA4BI element and/or diagram. The latter would result in an EA4BI element having a non-EA4BI composite diagram depicting the substructure.

The following example diagram illustrates both extensibility features:

• Association between an EA4BI process and ArchiMate (same level of detail)
• Decomposition of an EA4BI process by means of BPMN (higher level of detail)

 

 

As such, both extensibility features can be used to provide linkage to mainstream enterprise modeling languages such as ArchiMate, BPMN, UML, and data modeling. A coordinated application of these features can also result in extending EA4BI with organization-specific and/or industry-specific modeling policies and practices.